It has the "same voice" you say. But that is also, kind of, the same as saying that there is no humanity behind it, no identity, no "mind".

For lexical articles, like text given as an answer to a single prompt or question, AI writing can be excellent. Because, there we do want an answer that represents the average or combination of all human knowledge about the topic. We're not looking for personality, or another mind.

When we read long-form text, on the other hand, what we are engaging in, is mind-transfer. We then expect there to be a recognizable mind at the other end. And this is, in my opinion, what typically falls apart when we ask an AI to write a complete long-form text.

What about answers that an LLM gave to a question that we ourselves asked? Should we “labor” to understand that answer?

I think the argument, as presented in this and other similar pieces of critique, is too simplistic.

I do understand the criticism, but I think it should be framed in a different manner. The problem, when we read a long form piece by an author, is that we imagine that there’s another “mind” at the other side. We imagine that we are following the reasoning within the mind of a fellow human being, the writer. There’s an implied sort of “intimacy” to it. And the breach is when we are fooled into thinking that we are engaged in human communication, only to discover that there is a machine on the other side.

When we ask questions to an AI, this problem does not exist, because we are fully aware that the entity on the other side is not a human being.

Yet there is no doubt that the reply from an AI can contain information that is very much worthy of our time, and of our “labor” and effort to understand it.

So I think this ultimately will be about disclosure. As long as we are being made aware of the percentage of AI use in a text, explicitly or implicitly, I think we will actually grow to accept it.

Moreover, I've come to realize that my colleague is not adverse to using abstractions if they are well established, and if they already exist. But he is (much) less inclined to invent or write new abstractions than I am. As you have concluded, I have also concluded that this is actually a matter of cognitive style, more than it's a symptom of "slop" or "cargo-culting of best practices".

And today an LLM could probably have refactored it fairly well, automatically.

The movie Groundhog Day comes to mind.

(e) because it’s subjective.

The shapes look a bit wonky when projected onto a map, though, and it may not be as intuitive to reason about as the hexagons that would (mostly) result from subdividing an icosahedron. With a subdivided icosahedron you end up with a regular lattice of shapes that is easier to reason about. I think an icosahedron might be a better fit for an indexing scheme for that reason, despite it's higher mathematical error in approximating the sphere at a given resolution.

I explored a similar idea four or five years ago, without being aware of H3. My goal was to find a compact multi-resolution geospatial height map format. My idea was closer to H3 than to yours, it seems.

In other cases I believe it’s quite possible that our understanding of the world, say at age two, is so radically different from our adult understanding, that the old memories don’t even make any sense in the new context. They can’t be remapped in any meaningful manner.

  axios.get('https://api.example.com', {
      headers: { 'Authorization': 'Bearer token' },
      params: { key: 'value' }
  })
  .then(response => console.log(response.data))
  .catch(error => console.error(error));

Understanding it means understanding both your perspective on it, and other peoples' perspective on it, at the same time. That's most peoples' reason for wanting to interact with random people. They see it as an opportunity to refine their understanding of Thing, whatever it might be.