On the issue of AI:
The systems I've examined have been good at avoiding error if that error has been encountered in the past and is part of its dataset. The issue is not avoiding predictable errors, though. The core issue is that resolving a fresh problem may require a new solution. It may require a solution exceeding the constraints and limits of the programme design.
I return to my issue with the term AI. That first is enough for me. Artificial. Computers are brilliant at high speed processing. Millions, if not billions of times quicker than humans. Yet, as a dataset grows they suffer performance anxiety. One of the issues with 9/11 was not that the intelligence agencies had to little data in advance. The problem was that they had to much.
Armstrong had to fight on his descent to the Moon when the job entry subsystem became flooded with tasks.
Amdahl's law also has a place. As you add components to a computing system (and he specified additional processing power), the chatter and handshaking between them begins to overcome their capacity to perform the role they were expected to perform. They now exist for each other. They're no longer tallying bank balances or calculating an angle of attack. They're making sure that the system is fine.
IBM have designed massive database systems. CICS, IMS, DB2. 90%of the code is about resilience, recoverability, integrity. A small fraction does the job it is expected to do.