3 Theoretical Limits of AI - These Things Can't Be Fixed

A critical look at three fundamental, unfixable limitations of current LLM-based AI: (1) the learning ceiling problem — LLMs can't exceed the collective intelligence of their training data, especially as AI-generated content pollutes future training sets; (2) hallucination as an architectural inevitability — the same mechanism that enables creativity also produces confident incorrect outputs, and these can't be separated; (3) the frame problem — LLMs operate strictly within the context given to them and lack the ability to reframe a problem the way an experienced developer would. The author argues the truth lies between AI replacing developers and AI being useless, and that developers who understand these limits and use AI skillfully will gain a real productivity edge.