Couldn’t you still copy by training a new network on a new device to have same outputs for the same inputs as the original?
With ANN you can do it one time and then clone the result for negligible energy cost.
Maybe training a batch of PNNs in parallel could save some of the energy cost, but I don't know how feasible that is considering they could behave slightly differently during training causing divergence... Now that sarcastic comment at the bottom of this thread is starting to sound relevant "Schools".
That entirely depends on how many inferences the model will perform during its lifecycle. You can find different estimates for the energy consumption of ChatGPT, but they range from something like 500-1000 MWh a day. Assuming an electricity price of $0.165 per kWh, that would put you at roughly $80,000 to a $160,000 a day.
Even at the lower end of $80,000 a day, you'll reach your $4 Million in just 50 days.
With PNN you would have to multiply n by 1-4 million, training cost explodes.
Having to do that in each instance is still really cumbersome for cheap mass deployment compared to just making a digital-style exact copy, but then again I guess a main argument for wanting these systems is that they'd be doing things unachievable in practice on digital computers.
In some cases one might be able to distill to digital arithmetic after the heavy parts of the optimization are done, for replication, distribution, better access for software analysis, etc.