Photonic AI: Beyond Silicon

How does AI work today?

Today, AI operates through matrix multiplication techniques. These relatively heavy calculations are performed by what are called GPUs or TPUs. These allow for the calculation of astronomical amounts of data, but possess crucial disadvantages in their operating mode. Indeed, transporting electrons from memory chips to processing units consumes a tremendous amount of time and energy. Over 80% of the energy in a modern GPU is consumed simply by moving data, not by the calculation itself.

As you have surely understood, we are gradually reaching physical limits for this type of operation, via the transport of electrons from memory to computing center.

How does photonic AI work?

To address this bottleneck, several companies and researchers have developed a new method to reduce computing times, but above all, energy consumption. Several essential components must be mastered to achieve this.

First, Mach-Zehnder Interferometers. This is the basic building block of a photonic chip for processing matrices. A laser beam is divided, the phase on one arm (representing the mathematical weight) is adjusted, and then they are merged. The final light physically translates the multiply-accumulate operation. By assembling hundreds of MZIs, a network capable of 100% optical matrix calculations is created.

And then, Wavelength Division Multiplexing (WDM): Optics allow multiple frequencies (colors) to pass through a single waveguide without collision. This allows dozens of data streams to be injected in parallel, radically boosting bandwidth without impacting the physical footprint of the component.

What are the differences and advantages?

Which companies are pioneers in this field?

What risks and tipping points to watch?

To dodge violent drawdowns within this market undergoing a major shift, it is imperative to monitor several macro-technological risk variables:

The CPO (Co-Packaged Optics) pivot: Historically, the signal leaves the GPU via copper to reach a Pluggable Transceiver at the edge. The real game changer is CPO: integrating optics directly onto the compute substrate. Those who miss this shift towards hyper-integration will lose all commercial relevance.

Industrial Yield Rates: While standard silicon is ultra-mature (99% success), coupling InP lasers onto photonic chips is an engineering nightmare. A mediocre Yield immediately drags down margins and the financial viability of players in the sector.

The ADC/DAC bottleneck: Moving from light (analog) to electrons (digital) via energy-intensive converters remains the major weak point. A technological breakthrough on this specific link could disqualify current optical architectures overnight.