That's a stretch, since we don't know exactly how human learning works.

It is not a stretch. We may not know exactly how human learning works, but we do know exactly how machine learning works, and we know that it is not like how human learning works. It is absolutely possible to differentiate things even without complete knowledge.

For instance, I am not a biologist. I do not have complete knowledge of the workings of a horse or a snake. However, I do know that a horse and a snake are different.

I don't know about you, but I for one have not produced anything 'original' my entire coding career by that metric. I feel confident in saying the vast majority of programmers have not either.

This is a bad argument. The output of a generative model is a copy-and-paste function from a library of ingested code samples with a fairly competent keyword search attached to it. Code writing bots are just script kiddie crutches.

If all you did was copy and paste from GNU GPL code, then your output would also be bound by the same license.

But I disagree with the idea that their method of learning and their actions is inherently different from what the average person does.

Then you don't understand even the basics of the mathematics that makes them actually work. It's a purely algorithmic process. It's an outgrowth of multidimensional analysis and optimization, that's all.

Here is a better explanation than I could give, by someone who knows better than me:

Large Language Models explained briefly by 3Blue1Brown