What part of AI do you think we still fundamentally misunderstand?

I’ve been around software and data for long enough to have seen quite a few technology revolutions come and go. Most of them eventually turned out to involve rather more plumbing and rather less magic than the marketing suggested. AI is starting to look like the same story. I’m not particularly interested in another discussion about whether LLMs are impressive. They obviously are. What I’m more interested in is why so many perfectly good AI demos turn into rather mediocre production systems. I keep seeing teams jump straight into RAG, agents, fine-tuning, vector databases, tool calling, etc. Six months later they discover the less exciting problems were actually the important ones. Bad data. Poor evaluation. Unclear requirements. No one owning the output. And the small matter of what happens when the model confidently does the wrong thing. Those problems aren't nearly as exciting to talk about, but they tend to be the ones that cost you money. So what do you think is genuinely misunderstood at the moment? Could be something technical like context windows, retrieval, evaluation or fine-tuning. Could also be something more fundamental, like when an LLM shouldn't be used at all. I’m particularly interested in things you've actually encountered rather than textbook answers. What looked straightforward on paper and turned out to be a complete pain in production?