After a few days playing with this and using it for real work in some cases (having it bang out some PowerShell based on a description and follow-up modifications), I'm not sure that "the real kind of learning humans do" is even a necessary goal anymore.
Here is a language model that doesn't "know" anything, it doesn't "understand" anything, it has no idea what an AST is or what the code it is producing does… But does it really matter? If that prompt "generate a PowerShell script that does X Y and Z" results in accurate code that meets the stated requirement, how it got there is an implementation detail.
Give me what exists today, give it an ongoing knowledge of the things I am conversing with it on, take off the stupid guardrails and this is something I would gladly pay a significant amount of money every month for.
I disagree and I have worked on Google search ranking, making models that learn is ridiculously hard. This model is impressive, but it still hasn't solved this part, and until they do solve it the blocker isn't engineering effort but research effort with unknown timeframes.
When researches says a model "learns" all they mean is that they put the new data into the model, but the model is still as stupid as before so it doesn't really solve the real kind of learning humans do and the model would need to be able to do in order to be useful here.