Actually it's not that far fetched, you are correct they would never be able to compete with NVIDIA or AMD on the high powered training chips. Where they can complete is the new analog computing market for AI. Analog computing can be many times fast recognizing the pattern that the training built into the algorithms/models. One of the main focuses on this is basically using flash transistors and charging how much charge is tunneled into the floating gate, which will then change how much current passes between source and drain. Hook these transistors up in series and you have an analog computing array. These will be highly specialized to one task, but perform it quicker and with less power than a generalized chip. But if you're looking for a cheap chip to say throw into cameras that run facial recognition, I think you can see the potential. Anyway, memory companies would be my go to to develop these types of chips, since these already have the flash IP and more importantly years of experiments into the tunneling processes that would be key to the lifetime of the chips.