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Analog computers seem to be very well fit for running neural networks (that's since the beginning, this is not the fist time normal computers are beaten), but they are a really bad choice for mostly everything else.

In all likelihood, we will find many more usages for optical processors, but I really don't think they will ever be your main CPU.

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Analog computers can't easily be mutliplexed and the integration density of memory + other compute is nowhere near as high as current SRAM/DRAM. This might change with memristive crossbars, but that still doesn't solve the structural part of the problem, since most deep learning workloads are nowadays structurally very far from a feed-forward perceptron and dynamic execution etc. are absent from analog approaches.

Modern architectures are becoming simpler, usually just stacks of transformers, and they are good for text, vision, audio, or almost any task without special modifications. That means if they could implement an optical transformer that is 100x more efficient/faster everyone will want to have it.

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