I came to this same conclusion some years ago while working on a side project.
Before anything LLM existed, I built a site[0] to generate fake "AWS Blog Posts." I trained a markov chain generator on all AWS announcement posts up to that point, copied the html + css of aws's standard blog posts, then glued them all together with some python + JS. It turned out, IMO, pretty funny! People familiar with AWS's blog posts would often get several sentences in before they realized they were looking at word-soup.
When GPT was new, I looked into using that to "upgrade" it. I spent a weekend messing around with Minimaxir's gpt-2-simple generating blog posts based on AWS content. What I found was, ultimately, it was way less fun. The posts were far too realistic to be interesting. They read like totally-real blog posts that just happened to not be true.
I realized then that the humor of those early markov generations was the ridiculousness. The point where, a few words or sentences in, you realized it was all nonsense. LLM's these days are too good for that - the text they generate is sometimes wrong, but rarely nonsense in a humorous way.
Markov chain content was wrong in a "kid's say the darndest things" way, while modern LLMs are wrong in a "My uncle doesn't know basic geography" way.
I figured this same thing in a similar manner. I used to make markov chains of game patches and send them to the community. Fake dota patches were a huge hit, specially since those are usually huge. Among mostly non-sense or unfunny exaggeration ("this hero now has 500 armor"), there would usually be at least 5 or 6 extremely funny lines in there, if not stuff that was outright prophetic (Fiend's Grip creates additional 1/2/3 illusions).
LLM's did a number on that unfortunately. Major subreddits banned all AI content, mostly due to the sheer infestation of really boring Midjourney content sent out by naive users and bots. Without reach, I lost interest, so no more Markov Chains.
Before anything LLM existed, I built a site[0] to generate fake "AWS Blog Posts." I trained a markov chain generator on all AWS announcement posts up to that point, copied the html + css of aws's standard blog posts, then glued them all together with some python + JS. It turned out, IMO, pretty funny! People familiar with AWS's blog posts would often get several sentences in before they realized they were looking at word-soup.
When GPT was new, I looked into using that to "upgrade" it. I spent a weekend messing around with Minimaxir's gpt-2-simple generating blog posts based on AWS content. What I found was, ultimately, it was way less fun. The posts were far too realistic to be interesting. They read like totally-real blog posts that just happened to not be true.
I realized then that the humor of those early markov generations was the ridiculousness. The point where, a few words or sentences in, you realized it was all nonsense. LLM's these days are too good for that - the text they generate is sometimes wrong, but rarely nonsense in a humorous way.
Markov chain content was wrong in a "kid's say the darndest things" way, while modern LLMs are wrong in a "My uncle doesn't know basic geography" way.
[0] https://totes-not-amazon.com/ - click any link to get a new one.
LLM's did a number on that unfortunately. Major subreddits banned all AI content, mostly due to the sheer infestation of really boring Midjourney content sent out by naive users and bots. Without reach, I lost interest, so no more Markov Chains.