You are right but I think that there's a more interesting question: do humans stumble upon those large interesting/great algorithms in practice?
The key point here is that we are looking at algorithms already discovered in human history rather than enumerating all possible interesting algorithms. Of course there is an interesting algorithm that is very large, but humans don't discover them in practice. If you look up a list of greatest algorithms in history, they will be rather small in length. Many of them can be sketched in a whiteboard
I think that what is happening here is that our minds just can't hold billions of concepts at once. So if you have an algorithm with billions of things, it was most likely produced by a machine. Handcrafted things, on the other hand, are smaller in comparison
Another thing is that our minds like conceptual simplicity and view simplicity as a kind of beauty. So if we have a great algorithm but it is too large, we look for ways to express them in succinct ways (the right abstractions can help with that, and also help with understanding the algorithm better). We end up succeeding because the algorithms themselves had low Kolmogorov complexity (and thus, if they are too large they probably can be further compressed)
The key point here is that we are looking at algorithms already discovered in human history rather than enumerating all possible interesting algorithms. Of course there is an interesting algorithm that is very large, but humans don't discover them in practice. If you look up a list of greatest algorithms in history, they will be rather small in length. Many of them can be sketched in a whiteboard
I think that what is happening here is that our minds just can't hold billions of concepts at once. So if you have an algorithm with billions of things, it was most likely produced by a machine. Handcrafted things, on the other hand, are smaller in comparison
Another thing is that our minds like conceptual simplicity and view simplicity as a kind of beauty. So if we have a great algorithm but it is too large, we look for ways to express them in succinct ways (the right abstractions can help with that, and also help with understanding the algorithm better). We end up succeeding because the algorithms themselves had low Kolmogorov complexity (and thus, if they are too large they probably can be further compressed)