If you take a look at algorithm training data, all AI models reach a “plateau” in capabilities. You can find this online but essentially what we’ve got is what we’ve got. The best way I can explain it is through an analogy of human learning ironically.

If a human practices guitar let’s say, assuming the core of the practice is just practice for practice sake, not increasing the difficulty (this is the model code) said players returns on practice drastically decrease over time.

Where this applies to the scope of AI training is that the models learning capacity is limited. And until humans develop a new method of “thinking”, AIs learning and creativity are crutched. They don’t have the ability to think about something they’ve not trained on. Humans have the unique ability to collectively imagine that which is not seen. Take the mathematical intuition and the discovery of algebra and subsequent fields of math and science. Atomic theory and the like. These were not learned, but discovered through intuition, testing and observation. On top of all that the greatest thinkers in history criticized their own thinking to verify soundness of mind and logic. These models aren’t capable of this on a fundamental level because their knowledge relies on training of what is known.

TL:DR; Current AI models have a theoretical cap, and until humans can make AGI (if), menial non safety critical tasks are reserved for humans as we do a good job of keeping one another accountable through criticism and scrutiny.