It's a common and fair criticism. LLM-based products promise to save time, but for many complex, day-to-day tasks - adding a feature to a 50M LOC codebase, writ

It's a common and fair criticism. LLM-based products promise to save time, but for many complex, day-to-day tasks - adding a feature to a 50M LOC codebase, writing a magazine-quality article, properly summarizing 5 SEC filings - they often don't. They require careful re-validation, and once you find a few obvious issues, trust erodes and the whole thing gets tossed. This isn't a technology problem, it's a product problem - and one that may not be solvable with better models alone. Another issue: people communicate uncertainty naturally. We say "maybe", "it seems", "I'm not sure, but...". LLMs suppress that entirely, for structural reasons. The output sounds confident and polished, which warps perception - especially when the content is wrong.