There's no reason to think AI will stop improving, and the rate of improvement is increasing as well, and no reason to think that these tools won't vastly outperform us in the very near future. Putting aside AGI and ASI, simply improving the frameworks of instructions and context, breaking down problems into smaller problems, and methodology of tools will result in quality multiplication.

Making these sort of blanket assessments of AI, as if it were a singular, static phenomena is bad thinking. You can say things like "AI Code bad!" about a particular model, or a particular model used in a particular context, and make sense. You cannot make generalized statements about LLMs as if they are uniform in their flaws and failure modes.

They're as bad now as they're ever going to be again, and they're getting better faster, at a rate outpacing the expectations and predictions of all the experts.

The best experts in the world, working on these systems, have a nearly universal sentiment of "holy shit" when working on and building better AI - we should probably pay attention to what they're seeing and saying.

There's a huge swathe of performance gains to be made in fixing awful human code. There's a ton of low hanging fruit to be gotten by doing repetitive and tedious stuff humans won't or can't do. Those two things mean at least 20 or more years of impressive utility from AI code can be had.

Things are just going to get faster, and weirder, and weirder faster.