This is one of the more thoughtful, nuanced criticisms of the current LLM fad that I've read, and I'm delighted to see it make it show up on HN. The author starts off with a series of well-thought experiments that show Github Copilot generating _pretty valid_ frontend code, code that works and fulfills the prompt: but code that ignores every web accessibility rule of thumb in the most egregious ways. Sure, yes, bad web devs write bad code, and Copilot is -- on its best day -- a perfectly cromulent bad developer. Yawn, news at 11, etc.

But where he takes those examples and where his thoughts end up is where this essay really hit home for me:

> As more and more of the internet is generated by LLMs, more and more of it will reinforce biases. Then more and more LLMs will consume that biased content, use it for their own training, and the cycle will accelerate exponentially.

And 'biases' here isn't the usual "models are woke-lobotomized!" yammering, but rather a thoughtful take on how the use of LLMs for code generation may, at least for the current state of LLMs, slowly normalize _writing worse code_.