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‘Too little, too late’: critics perplexed and suspicious of AI leaders’ call for a slowdown
(www.theguardian.com)
this post was submitted on 14 Sep 2026
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Explain to me how the analogy is wrong then.
I have accepted that llms have hard limits, like you said with regards to information theory and godels incompleteness theorem. I don't have enough knowledge to articulate those limits, just like I don't know the physics that prevents you from shrinking the dye further but I accept that they exist.
So we agree there are hard limits, what I am saying is:
I'm not arguing where that "finish line" is because I know I don't know enough about that. I do understand that 99.999% of problems aren't passed that finish line and there's a lot to go before LLMs reach that line.
Almost all use cases for llms don't require creating novel math techniques, so why are you focusing on them? If "can't create genuinely new techniques" disqualifies something from being economically transformative or intellectually significant, that standard would disqualify most human knowledge labor too.