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Your whole logic is based on the idea that being able to do something means understanding that thing. This is simply wrong.
Humans feel emotions, yet they don't understand them. A calculator makes calculations, but no one would say that it understands math. People blink and breathe and hear, without any understanding of it.
The concept of understanding implies some form of meta-knowledge about the subject. Understanding math is more than using math, it's about understanding what you're doing and doing it out of intention. All of those things are absent in an AI, neural net or not. They cannot "see the world" because they need to be programmed specifically for a task to be able to do it; they are unable to actually grow out of their programming, which is what understanding would ultimately cause. They simply absorb data and spit it back out after doing some processing, and the fact that an AI can be made to produce completely incompatible results shows that there is nothing behind it.
That can be solved if you teach it the meta-knowledge with intermediary steps, for example:
It's hard to find such learning data though, but e.g. claude already uses intermediary steps. It preprocesses your input multiple times. It writes code, runs code to process your input, and that's still not the final response. Unfortunately, it's already smarter than some junior developers, and its consequence is worrying.