Humans have been optimizing for information transmission between individuals for a long time. Not just our hardware and neural nets have been optimized, but the structure of the information itself.
It is quite possible that you need such a large set of compex interactions to develop intelligence that intelligence can only develop in reality. It is possible that the process for doing this inherently inolves a chaotic system that makes duplicating or paralleling that development impossible. I don't see any reason why these artificial intelligences with divergent chaotically emergent capabilities would have to be able to teach each other things as easily as we do.
I've long been of the opinion culture / "social software" is vital for producing knowledge for humans and that this social software has been fine tuned and iterated upon for a very long time (and possibly co-developed with the evolution of our brain hardware so that both are co-specialized for each other). This software is likely very specialized to our brain structures and might not generalize well to other intelligence substrates. Thus even if we do develop neural network structres that can run on silicone and match human level intelligence, those entities would still need a lot of interaction with reality to develop their own culture that would allow them understand the world at the level we do. Edit: Is it even possible to efficiently run the necessary theories of human mind on non-human brains to operate effectively in our epistemic landscape? Communication requires huge amounts of inference using theories of mind to resolve ambiguity and add implicit information.
It is possible that we can "recompile" human knowledge to run on other types of intelligences, but I suspect this will be far more difficult that commonly expected. Just look at how hard (nigh impossible) it is today to even boostrap a new general purpose computer without depending on the output other general purpose computers.
We simply do not understand cognition or semantics well enough to know the answers to these questions adequately or with any degree of certainty.
Even if it does take an enormous investment and many years to train a computer to do a task, you can make as many copies as you want and everyone can have one. Educating people doesn’t have that kind of payoff.
That’s why you see billions spent in the effort to make a self-driving car. But it turns out that real-world experience isn’t enough, so far. Waymo has collected > 20 million miles of driving experience.
It’s not enough because machine learning is very inefficient at learning from experience. That could improve, though.
Something very weird would have to happen for it to be that hard.