I have similar opinion! Many people are not taking into account the fact that this learning technique significantly outscales the average in terms of numbers. While the very best may not be affected, the fact that it generates results that are on average or slightly below average is concerning. Additionally, its ability to imagine and contextually change random garbage means that there is already room for improvement. Given the compute and resources available to top labs, they will likely be able to add more context, which could exacerbate the problem.

To my knowledge, no other system has been able to retain context, imagine garbage, and refine it based on the given input context. For details, it can imagine a game, change the premise of the game and craft stories or lines based on the new context. It doesn't even have to be perfect. It is already capable of doing 60% job. Wait till it reaches 80%.

Now you do not have one skill, but you can have multitude of average looking skill on top of your own craft. Further, this thing provides a good entry point to dive into research and investigate. Just like how stable diffusion sparked curiosity on art culture, technicalities, photography, this can do it but on wider context and problems. Therefore, it is not the matter of will it, it is just the matter of when?