Most likely its training data included countless Space Invaders in various programming languages.
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We went from chatgpt's "oh, look, it looks like python code but everything is wrong" to "here's a full stack boilerplate app that does what you asked and works in 0-shot" inside 2 years. That's the kicker. And the sauce isn't just in the training set, models now do post-training and RL and a bunch of other stuff to get to where we are. Not to mention the insane abilities with extended context (first models were 2/4k max), agentic stuff, and so on.
These kinds of comments are really missing the point.
Even then, when you start to build up complexity within a codebase - the results have often been worse than "I'll start generating it all from scratch again, and include this as an addition to the initial longtail specification prompt as well", and even then... it's been a crapshoot.
I _want_ to like it. The times where it initially "just worked" felt magical and inspired me with the possibilities. That's what prompted me to get more engaged and use it more. The reality of doing so is just frustrating and wishing things _actually worked_ anywhere close to expectations.
The more I've used it, the more I've disliked how poor the results it's produced, and the more I've realised I would have been better served by doing it myself and following a methodical path for things that I didn't have experience with.
It's easier to step through a problem as I'm learning and making small changes than an LLM going "It's done, and production ready!" where it just straight up doesn't work for 101 different tiny reasons.