The explosive growth of cloud computing and AI – from generative, and reasoning, to (ultimately) agentic systems – is creating unprecedented demand for bandwidth, as the amount of data that must be moved both within and between data centers continues to surge. In turn, this exposes growing limitations in the underlying interconnects that not only span the data center fabric and rack level but also extend deep inside servers – down to where they link XPUs, memory, and other system components at the package and die level.

Copper-based (electrical) interconnects have long been the industry’s workhorse thanks to their low cost, simplicity, and reliability. Yet today, they are increasingly hitting fundamental limits: while highly energy-efficient electrical links can be realized, they are typically confined to very short reaches (on the order of millimeters). At longer distances and higher lane rates, power consumption rises sharply, thermal constraints intensify, and signal integrity degrades. In other words, electrical I/O is rapidly becoming a key barrier to scaling next-generation AI and machine learning systems.

Optical interconnects have emerged as a critical path forward. By transmitting data using light rather than electrical signals, they are far less sensitive to distance, enabling much better energy efficiency while mitigating many of the crosstalk and thermal challenges inherent to copper links. However, even optical technologies are now being pushed to new extremes – as the need emerges for (multi) terabit-per-second interfaces, ultra-high energy efficiency, and extremely low (sub-microsecond) latency.

Meeting these requirements will take more than incremental innovation.

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