A certain kind of talk around LLMs that I find increasingly puzzling

This is a certain kind of talk around LLMs that I find increasingly puzzling. That is all of the people bitching that LLMs constantly generate crap code and hallucinate solutions, and are worthless for programming. This has almost never happened to me, and never during the last two model generations I have used (chat GPT 5.4 and 5.5). Occasionally a model used to get a little deranged when I pushed its context limit, but under codex that doesn't happen anymore; instead I got a red-highlighted warning when the limit has been exceeded and I need to clear my session. I've applied AI to feature changes, refactoring, and debugging over 63 different projects written in C, Go, Rust, Python, and shell. I've written documentation with it. I've decompiled a DOS binary into readable source code. It's now routine that whenever I have to touch one of my projects I start by running the regression tests, then fire up codex and asking it to audit the code for bugs and suggest improvements. My experience is that LLMs are excellent and tremendously empowering tools. Their worst limitation is a kind of architectural tunnel vision - they're extremely good at generating code to specification but sometimes blind to higher-level patterns. Which is okay, it's my meatbrain job to be good at that. The most valuable thing I find about LLMs is exactly that they don't screw up details and edge cases. I'm a very, very good coder by human standards (I'd better be, with 50 years of experience!) but the LLMs are better than me. Because if a code change needs to touch (say) five places in the code, they reliably find all five rather than doing the human thing of fixing four and then having to debug for hours before you figure out that there's a fifth one you missed. Are the downshouters living in a different universe than me? Are they using old, weak models? Or do they have some kind of skill issue that I can't see because I have mental habits and communication skills that are a good fit for the handles on these tools? I don't know. And I think this is an important thing to figure out, because I'm seeing lots of stories in the news that suggest billions of dollars are being wasted on misdirected token spend. It all seems very simple to me. Be clear in your thinking, tell the model what you want with precision, and good things happen. What...what am I missing here?