In nearly every instance you will be citing stupidity in implementation. The limitations of generative AI in the present are related to access and scope along with the peripherals required to use them effectively. We are in a phase like the early microprocessor. By itself, a Z80 or 6502 was never a replacement for a PDP-11. It took many such processors and peripheral circuit blocks to make truly useful systems back in that era. The thing is, these microprocessors were Turing complete. It is possible to build them into anything if enough peripheral hardware is added and there is no limit on how many microprocessors are used.
Generative AI is fundamentally useful in a similar very narrow scope. The argument should be limited to the size and complexity required to access the needed utility and agentic systems along with the expertise and the exposure of internal IP to the most invasive and capable of potential competitors. If you are not running your own hardware infrastructure, assume everything shared is being archived with every unimaginable inference applied and tuned over time on the body of shared information. How well can anyone trust the biggest VC vampires in control of cloud AI.
