This isn't about scraping materials for training the underlying LLM or image gen models. This is about testing and training the agent frameworks that are given tasks to execute.

There’s so much AI slop out there that the frontier labs risk model collapse if they continue to train on it.

That’s why they’re resorting more and more on reinforcement learning. Give the AI a task. Quantify how well the AI performed the task. Less parroting what’s said on the Internet.

They’ll still scrape the internet. The difference is that the models will only have access to a cached version of the internet during the reinforcement learning process (for example, check the documentation of a programming framework). I’m surprised it took them this long to realize it’s a bad idea to do reinforcement learning with live internet access.

Joke theory:
This whole time, they've been testing in Prod and now they're hoping that there are enough users left to bury their contrived content because it'll be too difficult to delete it all.

Now they're moving back into Dev.

Real theory:
People are probably discovering exploits through real-world interactions with their models, so they're going back into their shell before someone declares "fair game."