>Besides, the workflow is designed for human hands and brains, not for AI.

If we want non human workflow we will need a massive amount of data on that for it to learn the correlation. But I'm at a loss of where anyone would even get data on a non human workflow. These specifically aren't thinking machines. They just know how to generate a point on a graph to look like the rest of the points. They're a really really good dart player. This is why I call them correlation engines. They can't replace workers on their own because the rules of the game change slightly and it'll be months or years of training before it's ready again.

>Humans are screwed then, for their brains are fairly limited.

Our neurons don't suffer the same issues because the sheer size of a human brain expressed as a neural network is larger than we can currently hope to compute yet somehow, training time is seconds and not years, and we have transfer learning at a scale that artificial networks don't have.

>It might be more reasonable to have no TPS reports at all as an example and come up with something that is better suited to AI capabilities.

We need data to train the AI on this new system. This means it will need millions of examples. Then it can spend a few years learning that data. We haven't even gotten into costs yet. Those instances will be costly to run. Newer models might be faster but they are not likely to invent time machines or sub atomic computers without examples of those things.