I've never relied on an LLM to build a large section of code but I can see why people might think it is worth a try. It is incredible for finding issues in the code that I write, arguably its best use-case. When I let it write a function on its own, it is often perfect and maybe even more concise and idiomatic than I would have been able to produce. It is natural to extrapolate and believe that whatever intelligence drives those results would also be able to handle much more.

It is surprising how bad it is at taking the lead given how effective it is with a much more limited prompt, particularly if you buy in to all the hype that it can take the place of human intelligence. It is capable of applying a incredible amount of knowledge while having virtually no real understanding of the problem.

LLMs do deliver "miracles", in certain cases, if you've experienced it and have been blown away by their output (one shot functional app from a well manufactured prompt, new feature added flawlessly on a complicated existing codebase, etc.), it can be tempting to reajust your expectations and think this will work consistently and at a much larger scale.

They can assimilate 100s of thousands of tokens of context in few seconds/minutes and do exceptional pattern matching beyond what any human can do, that's a main factor in why it looks like "miracles" to us. When a model actually solves a long standing issue that was never addressed due to a lack of funding/time/knowledge, it does feel miraculous and when you are exposed to this a couple of times it's easy to give them more trust, just like you would trust someone who provided you a helping hand a couple of times more than at total stranger.

Probably same reason people expected outsourcing to the cheapest firm in India would work: wishful thinking. People wanted it to work and therefore deluded themselves.

Or really the same reason people fall for get rich quick schemes.