Horizons of Electronics

Light can carry information, but it can also perform selected calculations inside a purpose-built circuit. Photonic computing is moving from isolated optical operations towards experimental systems that run neural networks. This review examines what recent demonstrations actually establish, from programmable tensor processors to tiny task-specific optical structures and computing-centre trials. Its central question is what these advances could change for artificial intelligence: useful computing capacity, response time, energy demand and cost. The answer depends on the whole system, including memory, conversion, precision, calibration and data movement. Published classification results are distinguished from general AI capability; projected power budgets are separated from measured consumption. The article explains why a fast optical operation does not guarantee a faster application, and why silicon can remain part of a photonic future. It identifies credible opportunities for hybrid and specialised accelerators while setting out the evidence still needed before broad efficiency or economic claims can be accepted.