If we are, indeed, in a virtuous cycle of LLMs building on each other, then we are actually in the knee of the curve before exponential increase in LLM capability.
An LLM that can access all other AI models (e.g., HuggingGPT) is not limited to the strengths and weaknesses of any one model. Declarations of "Peak LLM" or "LLMs can never be secured" are as laughable as statements like "Assembly can never be surpassed in abstraction".
What do you mean with this? There might never be a peak for something?
It doesn’t make much sense to me, so I read it as a flag that your position is more faith-based (or “hope-based” for a less loaded word) than fact-based. I could be wrong in this interpretation of course, so the initial question in my comment is a genuine one.
LLMs devouring the output of LLMs will only result in noise. They already make up garbage and it's only going to get worse.
10,000 LLMs doesn't fix that
Sparks of Artificial General Intelligence: Early experiments with GPT-4 https://arxiv.org/abs/2303.12712
LLMs exhibit emergent properties as they scale, we should assume the same will happen as we run divergent models in parallel.
By asking a rhetorical question and then refuting a position that wasn't asked is a Straw Man, the reference to 10k monkeys is a false analogy, your 10k LLMs answer to the question no one asked is a hasty generalization. How have you shown that 10k LLMs won't fix straw-problem?
So it seems that the chance of producing one of Shakespeare works no longer requires each work in the play to be randomly chosen in isolation, just enough correct word guesses to get the LLM into the groove.
"ChatGPT, please generate 100 random words, then interpret them as the beginning of a literary work and complete the work."
This is real progress. Many many monkeys may no longer be needed.