Down to the Last Meter

For forty years, the boundary where light beats electricity has been closing in; From the ocean floor to the city, the building, and the rack. It's now reached the inside of the computer itself.

In 1988, a consortium led by AT&T switched on TAT-8, the first transatlantic fiber optic cable, carrying ten times the capacity of the best copper cable ever laid across the ocean. Over those thousands of kilometers photons beat electrons so decisively that copper never competed at that distance again.

But the distance at which light beats electricity didn't stay at the ocean floor. It's been shrinking ever since. Fiber took the routes between cities, then the links between buildings, then the connections between rows of a data center, then the short hops between racks in the same room. At today's data rates, an electrical signal is barely survivable over a couple of meters of copper, and the frontier is still moving: the newest networking silicon converts to light inside the machine itself rather than at its edge.

Thinking is cheap, moving is expensive

Everyone carries the same mental model of a computer: there's a processor, which does the thinking, and there's the wiring, which does plumbing. The thinking is the hard part, the expensive part, the part that Moore's Law spent sixty years making better, while the wires just carry the results around.

That model has flipped. On a modern chip, a floating point operation, the basic unit of thinking, costs somewhere in the region of a picojoule, while fetching the two numbers that operation needs from memory a few centimeters away costs on the order of a thousand picojoules. The ratio has been measured at several hundred to one, and it's getting worse, because transistor physics keeps improving while wire physics doesn't. When Google published the energy accounting for one of its TPU generations, the arithmetic had gotten three times cheaper since the previous process node, but the cost of reaching into memory hadn't moved at all.

A modern AI accelerator spends a small minority of its energy doing the actual mathematics, and the majority hauling numbers from A to B. Across real workloads, data movement has been measured at 40 to 60 percent of total system energy. The machine spends more effort passing notes than doing homework, and every joule spent moving a bit arrives as heat that has to be removed from a rack. This is the pressure that has been pushing light inward for forty years: wherever moving a bit electrically costs more than converting it to photons, the photons eventually win.

Distance is the new clock speed

Once movement dominates, a strange thing happens to the definition of a computer: its performance becomes a function of geometry. How fast your machine runs depends on how far apart its parts are, and what medium connects them.

This matters right now because AI stopped fitting on single chips years ago. A frontier training run is tens of thousands of accelerators behaving as one machine, which means the network between them has become part of the processor rather than infrastructure around it. Once the wires became part of the machine, the machine inherited their physics.

Those physics are unforgiving. Electrical signaling over copper degrades with both distance and data rate, and the data rates have gone vertical. At 112 gigabits per second per lane, a copper cable can carry a clean signal about two and a half meters. At 224 gigabits, the generation now rolling into AI clusters, that reach collapses to about one meter. The distance from a chip to the far side of its own rack has become, electrically speaking, too far. Copper, which carried human communication for a hundred and fifty years, now can't reliably cross a server cabinet at the speeds AI demands.

The industry's answer for the past decade has been to convert to light at the edge of each switch: pluggable optical transceivers, small modules that take an electrical signal, turn it into laser light, and push it down a fiber. Inside each one sits a digital signal processor whose job is repairing the damage the signal suffered on its 20-centimeter electrical journey from the chip to the faceplate. That repair work alone accounts for roughly half the module's power, spent undoing what a short copper trip did to the signal rather than making light. It works, and it built the modern data center. But the conversion burns real power, roughly 30 watts for a state of the art 1.6 terabit port, and a large AI cluster needs hundreds of thousands of them. Jensen Huang has said that connecting a million GPUs with pluggable optics would consume around 180 megawatts just for the transceivers, more than the total power draw of most entire data centers. The cost had migrated to the conversion points at either end.

Co-packaged optics is the culmination of that forty-year iteration: instead of converting to light at a module plugged into the switch faceplate, the optical engines are placed on the same package as the switch silicon itself, millimeters from the transistors. The electrical path shrinks from tens of centimeters to almost nothing, and with it goes most of the amplification, retiming, and signal conditioning that made the old conversion so hungry. Nvidia's co-packaged photonic switches, the first of which reached the market in 2025 with Ethernet versions following through 2026, cut the power of that 1.6 terabit port from around 30 watts to 9 using a quarter of the lasers. A single switch replaces 72 pluggable transceivers. Microsoft, Meta, CoreWeave, and Oracle are among the first buyers, and TSMC has built a dedicated process platform for stacking photonics onto logic.

The caveat is that these first products change less than the headline numbers suggest. Networking is under a tenth of a cluster's total power, so even dramatic transceiver savings trim only a few percent off the whole. This generation partly serves as a rehearsal for the supply chain. The deeper shift arrives later this decade in the scale-up fabric, the links that let a rack of GPUs behave as one giant chip, where each GPU moves nearly ten times the data it sends across the wider network and copper's two-meter reach caps the size of the whole architecture.

What this is really about

This took a long time, and not because the physics was unfavorable. The hard problems were practical: attaching hair-thin fibers to silicon at manufacturing yield, keeping lasers alive next to hot chips, and servicing a switch whose optics can no longer be unplugged when a module dies. Co-packaged optics shipped once the packaging matured, years after the idea did, and the companies that solved fiber attach and laser reliability, problems with no glamour whatsoever, are the reason the roadmap works.

I find the whole arc clarifying for how to think about compute. The story we tell about progress in AI is a story about chips: transistor counts, process nodes, FLOPS. But the real limit has been drifting away from the arithmetic for years, first to memory, now to the network, always toward the movement of data rather than the transformation of it. The densest racks exist largely to keep dozens of GPUs inside copper's shrinking radius. The liquid cooling they demand is the price of huddling that close together. Cluster topologies are being redrawn around what distance costs. And photons keep getting pulled deeper into the machine, one crossover at a time, with silicon itself as the only territory left.

The next time someone shows you a chart of AI progress denominated in FLOPS, remember that the FLOPS are nearly free. The expensive thing, the thing entire industries are being rebuilt around, is carrying a number from one side of a building, or one side of a package, to the other. The capability roadmap of AI now runs as much through photonics, packaging, and thermal engineering as through transistor counts. Light shows no sign of stopping at the package edge: the next crossovers are already visible, between chiplets, between processors and memory, each one another retreat of the last meter of copper. From the outside, every one of them will look like the machines simply getting faster, and few will notice that what actually changed is how far a number can travel before the journey costs more than the thought.