Penn physicists led by Bo Zhen, with the work covered by ScienceDaily and Phys.org in late May, coupled light into a nanoscale cavity containing an atomically thin material to create exciton-polaritons — hybrid particles that are part photon and part electronic excitation. The team used these particles to perform all-light switching, the basic operation behind logic gates and neural-network activations, while consuming roughly four femtojoules per switch — about four quadrillionths of a joule, far below the energy needed to briefly power a small LED. The result was published in Physical Review Letters earlier this spring.
The mechanism matters. Conventional optical computing has been stuck for decades because photons do not naturally interact with each other strongly enough to compute — they pass through. By binding the photon to a matter excitation in a thin semiconductor, you get a particle that moves at near-light speed but interacts strongly enough to switch a signal. That is the bridge between the speed advantage of optics and the controllability of electronics, and it is the missing primitive for photonic AI chips that process light directly rather than converting it to current at every layer.
Energy is the live constraint on AI deployment in 2026. Frontier training runs are gated by gigawatt-class power contracts, and inference at consumer scale already strains data-center grids — which is why Anthropic, OpenAI, and the hyperscalers are signing decade-long nuclear and gas deals rather than buying more racks. A photonic neural network operating at femtojoule energies per operation would change the unit economics by orders of magnitude. Practical chips are years away, but the basic physics is now demonstrated in a working device.
Takeaway for learners: the bottleneck in AI is moving from algorithms to physics. The next decade of useful AI capability gain will come as much from semiconductor and photonics research as from new architectures, and the people building it are physicists and materials scientists. If you are choosing what to study, the boundary between AI and the underlying hardware is the most under-supplied talent market in the field.