It feels like these new models are no longer making order of magnitude jumps, but are instead into the long tail of incremental improvements. It seems like we might be close to maxing out what the current iteration of LLMs can accomplish and we're into the diminishing returns phase.<p>If that's the case, then I have a bad feeling for the state of our industry. My experience with LLMs is that their code does _not_ cut it. The hallucinations are still a serious issue, and even when they aren't hallucinating they do not generate quality code. Their code is riddled with bugs, bad architectures, and poor decisions.<p>Writing good code with an LLM isn't any faster than writing good code without it, since the vast majority of an engineer's time isn't spent writing -- it's spent reading and thinking. You have to spend more or less the same amount of time with the LLM understanding the code, thinking about the problems, and verifying its work (and then reprompting or redoing its work) as you would just writing it yourself from the beginning (most of the time).<p>Which means that all these companies that are firing workers and demanding their remaining employees use LLMs to increase their productivity and throughput are going to find themselves in a few years with spaghettified, bug-riddled codebases that no one understands. And competitors who _didn't_ jump on the AI bandwagon, but instead kept grinding with a strong focus on quality will eat their lunches.<p>Of course, there could be an unforeseen new order of magnitude jump. There's always the chance of that and then my prediction would be invalid. But so far, what I see is a fast approaching plateau.