Yep, because that's all AI is; a parrot. For well-defined problems it works great... BECAUSE THEIR WELL DEFINED.
Generative AI is only as good as the dataset that you give it, so for problems that exist in a heavily contrived and parameterized space (like leetcode-style problems) it works really well. But give it a novel problem with intertwining libraries and external dependencies along with custom type and structure definitions, it's going to fall flat.
Humans do something AI can't, and that's draw from experience to apply a solution on a novel problem. This is why I'm not terribly worried about AI coming for engineer jobs anywhere in the near future. I use ChatGPT all the time to write me small functions, generate regular expressions, etc. Basically all the drudgery.
I'd argue we've hit peak-AI at this point because from this point on, all datasets are going to be colored by AI-generated results. Generative-AI is now on a trajectory were it'll simply regress to the mean of knowledge.
Generative AI is only as good as the dataset that you give it, so for problems that exist in a heavily contrived and parameterized space (like leetcode-style problems) it works really well. But give it a novel problem with intertwining libraries and external dependencies along with custom type and structure definitions, it's going to fall flat.
Humans do something AI can't, and that's draw from experience to apply a solution on a novel problem. This is why I'm not terribly worried about AI coming for engineer jobs anywhere in the near future. I use ChatGPT all the time to write me small functions, generate regular expressions, etc. Basically all the drudgery.
I'd argue we've hit peak-AI at this point because from this point on, all datasets are going to be colored by AI-generated results. Generative-AI is now on a trajectory were it'll simply regress to the mean of knowledge.