A singular human does (or tends to). Humans as a group, where members join and leave a group with time, also do learn, but at a much slower pace - over the years to decades timeframe. "X things programmers should know about Y" is a template for quite a few very influential blog posts, yet for most of them, you find many programmers, even decades later, who don't actually know what they "should".

My experience was always that 90% of code is ugly and clunky. I'm not at all surprised, while reviewing AI-generated code, to see many of the same ugliness we regularly commit. The quality of the output code is now consistently average, which means it's basically shit in 90% of cases, but it tends to mostly work (in the general case). The same kind of shit I've seen people push to production thousands of times in my career.

We don't fully know how to write good code. We don't really understand what good code should objectively look like. Spending more time on code doesn't automatically lead to better code (but costs a lot more). Above all, we don't need good code - the business side is perfectly fine with "good enough right now" rather than "maybe a lot better half a year from now". And that's what the models are trained on. They would, indeed, need quite a lot of "emergent properties" to go from that to consistently good code. ASI-level properties, I suspect.

> Difference is, humans learn from their mistakes.

Great! So next time the human will prompt the agent to watch out for and avoid this bug.