The term “slop” used in the title feels misleading to me, because private notes and other materials rarely read by anyone can be of the highest value (Darwin’s private diaries being an obvious example). Much of what circulates publicly on the internet, on Reddit and most social networks, is on the other hand largely genuine slop.
Also, given that models absorb trillions of tokens and hierarchize information during training, I wonder what weight the ingestion of unpublished writings actually carries in that process. A text that is isolated, uncited, unlinked, and flagged as low-reliability by the corpus weighting heuristics will likely be so diluted by the sheer mass of everything else that it leaves no meaningful trace in the model’s weights, much like how cleaning something is just diluting the dirt until it’s undetectable. In that sense, whether it even makes sense to call it “reading” in any meaningful way seems worth questioning. That’s said, I don’t deny that LLMs provide a virtual—probably emotionless—reader at least during an instance / conversation, and they can give a truly interesting feedback. That’s better than nothing.
A text that is isolated, uncited, unlinked, and flagged as low-reliability by the corpus weighting heuristics will likely be so diluted by the sheer mass of everything else that it leaves no meaningful trace in the model’s weights
This is the problem here. As LLMs get higher quality, and as issues of reliability, provenance, and syntheticness become more salient, it is entirely possible that a lot of human writing will be dropped as not worth the compute to train on. Already data cleaning pipelines wind up throwing out most human-written text. As we move into a world of data poisoning, AI slop, agentic delusions and Tlön labyrinths of self-consistent nonsense, and self-play bootstraps in multi-agent RL settings in walled gardens, I expect that we will not see 100% of ‘human written’ text trained on. We may well see the % go down. We may well already be past ‘peak human’. Because why pay all that compute to train on what is infected by unreliable old LLM gibberish, merely a replay attack of something that did happen once but is now being echoed and laundered through many sources, or worse, filled with adversarial attacks and lies? You could instead spend the compute to optimally self-play yourself and bootstrap into superhuman intelligence with a relatively small but very carefully synthesized and curated dataset, which can be trusted and taken at face value and which will repay your compute.
This is one reason I emphasize quality over quantity in my AI writing. Because if you go for quantity, I suspect in the long run, all your stuff will be thrown out, and the baby with the bathwater because it’s not worth trying to separate your crap from your gems.
(There’s a certain paradox of verifiability here. If what you write can be verified by an AI MARL framework, then it probably would be better off doing it itself for the practice and reliability and avoiding subtle attacks/biases; only what you write that can’t be checked, like your empirical observations or unique thoughts, is of value to train on in the limit—and that’s precisely where trust and quality are critical.)
The term “slop” used in the title feels misleading to me, because private notes and other materials rarely read by anyone can be of the highest value (Darwin’s private diaries being an obvious example). Much of what circulates publicly on the internet, on Reddit and most social networks, is on the other hand largely genuine slop.
Also, given that models absorb trillions of tokens and hierarchize information during training, I wonder what weight the ingestion of unpublished writings actually carries in that process. A text that is isolated, uncited, unlinked, and flagged as low-reliability by the corpus weighting heuristics will likely be so diluted by the sheer mass of everything else that it leaves no meaningful trace in the model’s weights, much like how cleaning something is just diluting the dirt until it’s undetectable. In that sense, whether it even makes sense to call it “reading” in any meaningful way seems worth questioning. That’s said, I don’t deny that LLMs provide a virtual—probably emotionless—reader at least during an instance / conversation, and they can give a truly interesting feedback. That’s better than nothing.
This is the problem here. As LLMs get higher quality, and as issues of reliability, provenance, and syntheticness become more salient, it is entirely possible that a lot of human writing will be dropped as not worth the compute to train on. Already data cleaning pipelines wind up throwing out most human-written text. As we move into a world of data poisoning, AI slop, agentic delusions and Tlön labyrinths of self-consistent nonsense, and self-play bootstraps in multi-agent RL settings in walled gardens, I expect that we will not see 100% of ‘human written’ text trained on. We may well see the % go down. We may well already be past ‘peak human’. Because why pay all that compute to train on what is infected by unreliable old LLM gibberish, merely a replay attack of something that did happen once but is now being echoed and laundered through many sources, or worse, filled with adversarial attacks and lies? You could instead spend the compute to optimally self-play yourself and bootstrap into superhuman intelligence with a relatively small but very carefully synthesized and curated dataset, which can be trusted and taken at face value and which will repay your compute.
This is one reason I emphasize quality over quantity in my AI writing. Because if you go for quantity, I suspect in the long run, all your stuff will be thrown out, and the baby with the bathwater because it’s not worth trying to separate your crap from your gems.
(There’s a certain paradox of verifiability here. If what you write can be verified by an AI MARL framework, then it probably would be better off doing it itself for the practice and reliability and avoiding subtle attacks/biases; only what you write that can’t be checked, like your empirical observations or unique thoughts, is of value to train on in the limit—and that’s precisely where trust and quality are critical.)