I've often called slop "signal-shaped noise". I think the damage already done by slop pissed all over the reservoirs of knowledge, art and culture is irreversible and long-lasting. This is the only thing generative "AI" is good at, making spam that's hard to detect.
It occurs to me that one way to frame this technology is as a precise inversion of Bayesian spam filters for email; no more and no less. I remember how it was a small revolution, in the arms race against spammers, when statistical methods came up; everywhere we took of the load of straining SpamAssassin with rspamd (in the years before gmail devoured us all). I would argue "A Plan for Spam" launched Paul Graham's notoriety, much more than the Lisp web stores he was so proud of. Filtering emails by keywords was not being enough, and now you could train your computer to gradually recognise emails that looked off, for whatever definition of "off" worked for your specific inbox.
Now we have the richest people building the most expensive, energy-intensive superclusters to use the same statistical methods the other way around, to generate spam that looks like not-spam, and is therefore immune to all strategies we developed. That same blob-like malleability of spam filters make the new spam generators able to fit their output to whatever niche they want to pollute; the noise can be shaped like any signal.
I wonder what PG is saying about gen-"AI" these days? let's check:
“AI is the exact opposite of a solution in search of a problem,” he wrote on X. “It’s the solution to far more problems than its developers even knew existed … AI is turning out to be the missing piece in a large number of important, almost-completed puzzles.”
He shared no examples, but […]
Who would have thought that A Plan for Spam was, all along, a plan for spam.
