Two sad-funny examples of how, nah, we're exactly that dumb. The first, from TDWTF, points out the fundamental problem with training a machine-learning system how to write software:
Any ML system is only as good as its training data, and this leads to some seriously negative outcomes. We usually call this algorithmic bias, and we all know the examples. It's why voice assistants have a hard time with certain names or accents. It's why sentencing tools for law enforcement mis-classify defendants. It's why facial recognition systems have a hard time with darker skin tones.
In the case of an ML tool that was trained on publicly available code, there's a blatantly obvious flaw in the training data: MOST CODE IS BAD.
If you feed a big pile of Open Source code into OpenAI, the only thing you're doing is automating the generation of bad code, because most of the code you fed the system is bad. It's ironic that the biggest obstacle to automating programmers out of a job is that we are terrible at our jobs.
I regret to inform the non-programmer portion of the world that this is true.
But still, most of the world's bad code isn't nearly as bad as the deposition Paula Deen gave in her harassment suit in May 2013. This came up in a conversation over the weekend, and the person I discussed this with insisted that, no, she really said incredibly dumb things that one has to imagine made her attorney weep. She reminds us that the Venn diagram of casual bigotry and stupidity has a large overlapping area labeled "Murica."
Just wait for the bit where the plaintiff's attorney asks Deen to give an example of a nice way to use the N-word.
I will now continue writing code I hope never winds up in either a deposition or on TDWTF.