
The startup aims to reduce data latency by 50% to reshape data center geography and AI infrastructure.
AI-generated summary
Data center developers are facing significant power and political constraints, forcing them to distribute compute across multiple campuses.
Data center developers are expected to spend as much as $4 trillion by the end of the decade — and they’re already heavily constrained by both political and power-grid considerations in where they can build. But while most treat the speed of fiber as a given, one company is betting that faster fiber could change the geographical math behind the data center buildout.
On Tuesday, Relativity Networks announced $22 million in SAFE note funding drawn by Rhapsody Venture Partners, Bell Ventures Inc., and Faster Than Glass LLC, among others. A SAFE note, in which an investment transfers into a specific numbers of shares once the company raises its first priced round, is a standard method used for pre-seed and seed rounds. The company also secured a $40 million follow-on order from a leading hyperscaler that declined to be named for this piece.
Relativity Networks deals in hollow-core fiber, a rarely deployed technology that allows data to be transmitted 50% faster than conventional fiber. Where traditional fiber transmits light through fiber-optic glass, hollow-core fiber transmits the same light through a vacuum chamber in the center of the line, bringing it far closer to the theoretical limit of light speed.
The difference is a matter of microseconds. CEO Jason Eichenholz estimates that a signal takes roughly five microseconds to travel one kilometer in conventional fiber. By switching to hollow-core, that figure can be reduced to only three and a half microseconds.
When AI compute occurred across a single rack of GPUs, the fiber latency was easy to ignore — but as scale has grown, so has the physical distance between GPUs. Now, it’s common for a data center campus to sprawl across hundreds of acres and dozens of buildings. Eichenholz sees a particular opportunity for multi-campus deployments, in which pre-existing data centers are knit together to operate as a single unit.
“The largest systems are distributing the compute across multiple campuses to reach the power that exists,” he tells TechCrunch. “They’re moving to where the warm shell is, but they still need to operate as one synchronized machine.”
The result is a way to partially alleviate the harsh spatial logic that has restrained many ongoing data center buildouts. In latency terms, reducing time by 50% is giving developers an opportunity to span 50% larger distances before latency becomes a problem. As compute projects scale ever larger, Einholz thinks it could be a major shift for the industry.
“The first era of AI optimized for compute,” he said. “It was GPU, GPU, GPU. The second era optimized the networking inside the data center to take advantage of that compute. The third era that we see coming is optimizing the geography.”

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