Imagine that current AI is already curating and generating datasets for the next generation.
Also consider that what we have now is only possible because hardware capability increased.
This was pretty much refuted by Meta with their LLama3 release. Two key points I got from a podcast with the lead data person, right after release:
a) Internet data is generally shit anyway. Previous generations of models are used to sift through, classify and clean up the data
b) post-processing (aka finetuning) uses mostly synthetic datasets. Reward models based on human annotators from previous runs were already outperforming said human annotators, so they just went with it.
This also invalidates a lot of the early "model collapse" findings when feeding the model's output to itself. It seems that many of the initial papers were either wrong, used toy models, or otherwise didn't use the proper techniques to avoid model collapse (or, perhaps they wanted to reach it...)
So yes we will see manual labor to finetune the data lair but this will only be necessary for a certain amount of time. And in parallel we also help by just using it: With the feedback we give these systems.
A feedback loop mechanism is fundamental part of AI ecosystems.
On the contrary, human annotation work is stepping up now because we create so many more prompts and want to test them.
There are crucial quality issues with Mechanical Turk, though, and when these really start damaging AI in obvious ways, the system (and the compensation, vetting procedures and oversight) seems likely to change.
More seriously, the output quality of LLMs for code is pretty inconsistent. I think there's an analogy to be made with literature. For instance, a short story generated by an LLM can't really hold a candle to the work of a human author.
LLM-generated code can be a good starting point for avoiding tedious aspects of software development, like boilerplate or repetitive tasks. When it works, it saves a lot of time. For example, if I need to generate a bunch of similar functions, an LLM can sometimes act like an ad-hoc code generator, helping to skip the manual labor. I’ve also gotten some helpful suggestions on code style, though mostly for small snippets. It’s especially useful for refreshing things you already know—like quickly recalling "How do I do this with TypeScript?" without needing to search for documentation.
Anyway, literature writers and software engineers aren't going to be replaced anytime soon.