Pro-AI attempts to portray the generalization that AI actually does as "AI learns like a human" are counterproductive to the goal, because it is not at all.

It's a bit difficult to talk about pretraining because there's a huge amount of data involved, so it's better to take as an example when the AI is already trained and in use.

For example paper from 2025 years about in-context learning ( so about what you can get by simple give chatgpt new information in chat , it is even not about training) . The authors find that, when you give a Transformer a few examples for a new (out-of-distribution) task, it usually doesn’t truly learn a brand-new rule. Instead, it tends to fall back on the kinds of rules it already saw during pretraining and tries to get the best possible answer within them. (https://proceedings.iclr.cc/paper\_files/paper/2025/file/cf7a83a5342befd11d3d65beba1be5b0-Paper-Conference.pdf)

AI clearly exhibits non-zero adaptation, not enough to understand new rules, but enough to be flexible as long as what you want is remotely similar to what you're looking for. Calling this "learning like a human" is ridiculous. Calling it "AI copying its training data" is even funnier.

Within the framework of your dialogue with the AI, the AI shows a certain adaptation and can learn to better use what it has already been trained to do.