You have a pretty interesting idea that I hadn't heard elsewhere. Do you know if there's been any research to make an AI model learn that way?
In my own time while I've messed around with some ML stuff, I've heard of approaches where you try to get the model to accomplish progressively more complex tasks but in the same domain. For example, if you wanted to train a model to control an agent in a physics simulation to walk like a humanoid you'd have it learn to crawl first, like a real human. I guess for an AGI it makes sense that you would have it try to learn a model of the world across different domains like vision, or sound. Heck, since you can plug any kind of input to it you could have it process radio, infrared, whatever else. That way it could have a very complete model of the world.
It won't be long (maybe 3 years max) before industry adopts some technique for automatically prompting a LLM to generate code to fulfill a certain requirement, then iteratively improve it using test data to get it to pass all test cases. And I'm pretty sure there already are ways to get LLM's to generate test cases. So this could go nightmarishly wrong very very fast if industry adopts that technology and starts integrating hundreds of unnecessary libraries or pieces of code that the AI just learned to "spam" everywhere so to speak. These things are way dumber than we give them credit for.